# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
nltk.download('all')
[nltk_data] Downloading collection 'all' [nltk_data] | [nltk_data] | Downloading package abc to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package abc is already up-to-date! [nltk_data] | Downloading package alpino to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package alpino is already up-to-date! [nltk_data] | Downloading package biocreative_ppi to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package biocreative_ppi is already up-to-date! [nltk_data] | Downloading package brown to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown is already up-to-date! [nltk_data] | Downloading package brown_tei to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown_tei is already up-to-date! [nltk_data] | Downloading package cess_cat to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package cess_cat is already up-to-date! 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[nltk_data] | [nltk_data] Done downloading collection all
True
# path = '/content/drive/MyDrive/Files/'
path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
df_movies = pd.read_csv(path + 'ottmovies.csv')
df_movies.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13+ | 8.8 | 87% | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 1 | 2 | The Matrix | 1999 | 16+ | 8.7 | 88% | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 2 | 3 | Avengers: Infinity War | 2018 | 13+ | 8.4 | 85% | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 3 | 4 | Back to the Future | 1985 | 7+ | 8.5 | 96% | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16+ | 8.8 | 97% | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161.0 | movie | NaN | 1 | 0 | 1 | 0 | 0 |
# profile = ProfileReport(df_movies)
# profile
def data_investigate(df):
print('No of Rows : ', df.shape[0])
print('No of Coloums : ', df.shape[1])
print('**'*25)
print('Colums Names : \n', df.columns)
print('**'*25)
print('Datatype of Columns : \n', df.dtypes)
print('**'*25)
print('Missing Values : ')
c = df.isnull().sum()
c = c[c > 0]
print(c)
print('**'*25)
print('Missing vaules %age wise :\n')
print((100*(df.isnull().sum()/len(df.index))))
print('**'*25)
print('Pictorial Representation : ')
plt.figure(figsize = (10, 10))
sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
plt.show()
data_investigate(df_movies)
No of Rows : 16923
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb float64
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime float64
Kind object
Seasons float64
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
dtype: object
**************************************************
Missing Values :
Age 8457
IMDb 328
Rotten Tomatoes 10437
Directors 357
Cast 648
Genres 234
Country 303
Language 437
Plotline 4958
Runtime 382
Seasons 16923
dtype: int64
**************************************************
Missing vaules %age wise :
ID 0.000000
Title 0.000000
Year 0.000000
Age 49.973409
IMDb 1.938191
Rotten Tomatoes 61.673462
Directors 2.109555
Cast 3.829108
Genres 1.382734
Country 1.790463
Language 2.582284
Plotline 29.297406
Runtime 2.257283
Kind 0.000000
Seasons 100.000000
Netflix 0.000000
Hulu 0.000000
Prime Video 0.000000
Disney+ 0.000000
Type 0.000000
dtype: float64
**************************************************
Pictorial Representation :
# ID
# df_movies = df_movies.drop(['ID'], axis = 1)
# Age
df_movies.loc[df_movies['Age'].isnull() & df_movies['Disney+'] == 1, "Age"] = '13'
# df_movies.fillna({'Age' : 18}, inplace = True)
df_movies.fillna({'Age' : 'NR'}, inplace = True)
df_movies['Age'].replace({'all': '0'}, inplace = True)
df_movies['Age'].replace({'7+': '7'}, inplace = True)
df_movies['Age'].replace({'13+': '13'}, inplace = True)
df_movies['Age'].replace({'16+': '16'}, inplace = True)
df_movies['Age'].replace({'18+': '18'}, inplace = True)
# df_movies['Age'] = df_movies['Age'].astype(int)
# IMDb
# df_movies.fillna({'IMDb' : df_movies['IMDb'].mean()}, inplace = True)
# df_movies.fillna({'IMDb' : df_movies['IMDb'].median()}, inplace = True)
df_movies.fillna({'IMDb' : "NA"}, inplace = True)
# Rotten Tomatoes
df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].astype(int)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].mean()}, inplace = True)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].median()}, inplace = True)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'].astype(int)
df_movies.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
# Directors
# df_movies = df_movies.drop(['Directors'], axis = 1)
df_movies.fillna({'Directors' : "NA"}, inplace = True)
# Cast
df_movies.fillna({'Cast' : "NA"}, inplace = True)
# Genres
df_movies.fillna({'Genres': "NA"}, inplace = True)
# Country
df_movies.fillna({'Country': "NA"}, inplace = True)
# Language
df_movies.fillna({'Language': "NA"}, inplace = True)
# Plotline
df_movies.fillna({'Plotline': "NA"}, inplace = True)
# Runtime
# df_movies.fillna({'Runtime' : df_movies['Runtime'].mean()}, inplace = True)
# df_movies['Runtime'] = df_movies['Runtime'].astype(int)
df_movies.fillna({'Runtime' : "NA"}, inplace = True)
# Kind
# df_movies.fillna({'Kind': "NA"}, inplace = True)
# Type
# df_movies.fillna({'Type': "NA"}, inplace = True)
# df_movies = df_movies.drop(['Type'], axis = 1)
# Seasons
# df_movies.fillna({'Seasons': 1}, inplace = True)
# df_movies.fillna({'Seasons': "NA"}, inplace = True)
df_movies = df_movies.drop(['Seasons'], axis = 1)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# df_movies.fillna({'Seasons' : df_movies['Seasons'].mean()}, inplace = True)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# Service Provider
df_movies['Service Provider'] = df_movies.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_movies.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)
# Removing Duplicate and Missing Entries
df_movies.dropna(how = 'any', inplace = True)
df_movies.drop_duplicates(inplace = True)
data_investigate(df_movies)
No of Rows : 16923
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
'Service Provider'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb object
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime object
Kind object
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
Service Provider object
dtype: object
**************************************************
Missing Values :
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :
ID 0.0
Title 0.0
Year 0.0
Age 0.0
IMDb 0.0
Rotten Tomatoes 0.0
Directors 0.0
Cast 0.0
Genres 0.0
Country 0.0
Language 0.0
Plotline 0.0
Runtime 0.0
Kind 0.0
Netflix 0.0
Hulu 0.0
Prime Video 0.0
Disney+ 0.0
Type 0.0
Service Provider 0.0
dtype: float64
**************************************************
Pictorial Representation :
df_movies.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 3 | 4 | Back to the Future | 1985 | 7 | 8.5 | 96 | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16 | 8.8 | 97 | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
df_movies.describe()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| count | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.0 |
| mean | 8462.000000 | 2003.211901 | 0.214915 | 0.062637 | 0.727235 | 0.033150 | 0.0 |
| std | 4885.393638 | 20.526532 | 0.410775 | 0.242315 | 0.445394 | 0.179034 | 0.0 |
| min | 1.000000 | 1901.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 |
| 25% | 4231.500000 | 2001.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 |
| 50% | 8462.000000 | 2012.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.0 |
| 75% | 12692.500000 | 2016.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.0 |
| max | 16923.000000 | 2020.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.0 |
df_movies.corr()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| ID | 1.000000 | -0.217816 | -0.644470 | -0.129926 | 0.469301 | 0.263530 | NaN |
| Year | -0.217816 | 1.000000 | 0.256151 | 0.101337 | -0.255578 | -0.047258 | NaN |
| Netflix | -0.644470 | 0.256151 | 1.000000 | -0.118032 | -0.745141 | -0.089649 | NaN |
| Hulu | -0.129926 | 0.101337 | -0.118032 | 1.000000 | -0.284654 | -0.039693 | NaN |
| Prime Video | 0.469301 | -0.255578 | -0.745141 | -0.284654 | 1.000000 | -0.289008 | NaN |
| Disney+ | 0.263530 | -0.047258 | -0.089649 | -0.039693 | -0.289008 | 1.000000 | NaN |
| Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# df_movies.sort_values('Year', ascending = True)
# df_movies.sort_values('IMDb', ascending = False)
# df_movies.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_ottmovies.csv', index = False)
# path = '/content/drive/MyDrive/Files/'
# udf_movies = pd.read_csv(path + 'updated_ottmovies.csv')
# udf_movies
# df_netflix_movies = df_movies.loc[(df_movies['Netflix'] > 0)]
# df_hulu_movies = df_movies.loc[(df_movies['Hulu'] > 0)]
# df_prime_video_movies = df_movies.loc[(df_movies['Prime Video'] > 0)]
# df_disney_movies = df_movies.loc[(df_movies['Disney+'] > 0)]
df_netflix_only_movies = df_movies[(df_movies['Netflix'] == 1) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_hulu_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 1) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_prime_video_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 1 ) & (df_movies['Disney+'] == 0)]
df_disney_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 1)]
df_movies_years = df_movies.copy()
df_movies_years.drop(df_movies_years.loc[df_movies_years['Year'] == "NA"].index, inplace = True)
# df_movies_years = df_movies_years[df_movies_years.Year != "NA"]
df_movies_years['Year'] = df_movies_years['Year'].astype(int)
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_years_movies = df_movies_years.loc[df_movies_years['Netflix'] == 1]
hulu_years_movies = df_movies_years.loc[df_movies_years['Hulu'] == 1]
prime_video_years_movies = df_movies_years.loc[df_movies_years['Prime Video'] == 1]
disney_years_movies = df_movies_years.loc[df_movies_years['Disney+'] == 1]
df_movies_years_group = df_movies_years.copy()
plt.figure(figsize = (10, 10))
corr = df_movies_years.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()
<Figure size 720x720 with 0 Axes>
df_years_high_movies = df_movies_years.sort_values(by = 'Year', ascending = False).reset_index()
df_years_high_movies = df_years_high_movies.drop(['index'], axis = 1)
# filter = (df_movies_years['Year'] == (df_movies_years['Year'].max()))
# df_years_high_movies = df_movies_years[filter]
# highest_rated_movies = df_movies_years.loc[df_movies_years['Year'].idxmax()]
print('\nMovies with Highest Ever Year are : \n')
df_years_high_movies.head(5)
Movies with Highest Ever Year are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 761 | The Occupant | 2020 | 18 | 6.4 | 50 | David Pastor,Àlex Pastor | Javier Gutiérrez,Mario Casas,Bruna Cusí,Ruth D... | Adventure,Drama,Thriller | Spain | Spanish | NA | 103 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 1 | 706 | Go! | 2020 | 16 | 7.2 | 88 | Doug Liman | Katie Holmes,Sarah Polley,Suzanne Krull,Desmon... | Comedy,Crime | United States | English | Told from three perspectives, a story of a bun... | 102 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 2 | 682 | Horse Girl | 2020 | 16 | 5.9 | 70 | Jeff Baena | Alison Brie,Molly Shannon,Goldenite,Stella Che... | Drama,Mystery,Thriller | United States | English | Sarah, a socially isolated arts and crafts sto... | 103 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 3 | 2325 | The Last Thing He Wanted | 2020 | 16 | 4.3 | 5 | Dee Rees | Anne Hathaway,Ben Affleck,Rosie Perez,Willem D... | Crime,Drama,Thriller | United States | English,Spanish,French | NA | 115 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 4 | 6544 | Shubh Mangal Zyada Saavdhan | 2020 | NR | 5.8 | 92 | Hitesh Kewalya | Ayushmann Khurrana,Jitendra Kumar,Gajraj Rao,N... | Comedy,Romance | India | Hindi | An eccentric marketing guru visits a Coca-Cola... | 117 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
fig = px.bar(y = df_years_high_movies['Title'][:15],
x = df_years_high_movies['Year'][:15],
color = df_years_high_movies['Year'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Year : In Minutes'},
title = 'Movies with Highest Year in Minutes : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_years_low_movies = df_movies_years.sort_values(by = 'Year', ascending = True).reset_index()
df_years_low_movies = df_years_low_movies.drop(['index'], axis = 1)
# filter = (df_movies_years['Year'] == (df_movies_years['Year'].min()))
# df_years_low_movies = df_movies_years[filter]
print('\nMovies with Lowest Ever Year are : \n')
df_years_low_movies.head(5)
Movies with Lowest Ever Year are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16756 | Space: The New Frontier | 1901 | 16 | 7 | NA | Dave Bullock | David Boreanaz,Miguel Ferrer,Neil Patrick Harr... | Animation,Action,Adventure,Fantasy,Sci-Fi | United States | English | The American Muscle Car series relives that in... | 75 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 1 | 4343 | A Trip to the Moon | 1902 | 0 | 8.2 | 44 | Georges Méliès | Victor André,Bleuette Bernon,Brunnet,Jehanne d... | Short,Adventure,Comedy,Fantasy,Sci-Fi | France | None,French | An association of astronomers has convened to ... | 13 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 2 | 8557 | From the Manger to the Cross | 1912 | 7 | 5.7 | NA | Sidney Olcott | R. Henderson Bland,Percy Dyer,Gene Gauntier,Al... | Biography,Drama | United States | None,English | NA | 60 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 3 | 9636 | Fatty Joins the Force | 1913 | NR | 5.3 | NA | George Nichols | Roscoe 'Fatty' Arbuckle,Charles Avery,Lou Bres... | Comedy,Short | United States | None,English | 7 years after the original Fortress movie, Bre... | 12 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 4 | 9884 | The Speed Kings | 1913 | NR | 5 | NA | Wilfred Lucas | Ford Sterling,Mabel Normand,Teddy Tetzlaff,Ear... | Short,Action,Comedy | United States | None,English | In Fort Hernandez, San Antonio, a group of Mex... | 8 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
fig = px.bar(y = df_years_low_movies['Title'][:15],
x = df_years_low_movies['Year'][:15],
color = df_years_low_movies['Year'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Year : In Minutes'},
title = 'Movies with Lowest Year in Minutes : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{df_movies_years['Year'].unique().shape[0]}' unique Year s were Given, They were Like this,\n
{df_movies_years.sort_values(by = 'Year', ascending = False)['Year'].unique()}\n
The Highest Ever Year Ever Any Movie Got is '{df_years_high_movies['Title'][0]}' : '{df_years_high_movies['Year'].max()}'\n
The Lowest Ever Year Ever Any Movie Got is '{df_years_low_movies['Title'][0]}' : '{df_years_low_movies['Year'].min()}'\n
''')
Total '110' unique Year s were Given, They were Like this,
[2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007
2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993
1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979
1978 1977 1976 1975 1974 1973 1972 1971 1970 1969 1968 1967 1966 1965
1964 1963 1962 1961 1960 1959 1958 1957 1956 1955 1954 1953 1952 1951
1950 1949 1948 1947 1946 1945 1944 1943 1942 1941 1940 1939 1938 1937
1936 1935 1934 1933 1932 1931 1930 1929 1928 1927 1926 1925 1924 1923
1922 1921 1920 1919 1918 1917 1916 1915 1913 1912 1902 1901]
The Highest Ever Year Ever Any Movie Got is 'The Occupant' : '2020'
The Lowest Ever Year Ever Any Movie Got is 'Space: The New Frontier' : '1901'
netflix_years_high_movies = df_years_high_movies.loc[df_years_high_movies['Netflix']==1].reset_index()
netflix_years_high_movies = netflix_years_high_movies.drop(['index'], axis = 1)
netflix_years_low_movies = df_years_low_movies.loc[df_years_low_movies['Netflix']==1].reset_index()
netflix_years_low_movies = netflix_years_low_movies.drop(['index'], axis = 1)
netflix_years_high_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 761 | The Occupant | 2020 | 18 | 6.4 | 50 | David Pastor,Àlex Pastor | Javier Gutiérrez,Mario Casas,Bruna Cusí,Ruth D... | Adventure,Drama,Thriller | Spain | Spanish | NA | 103 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 1 | 706 | Go! | 2020 | 16 | 7.2 | 88 | Doug Liman | Katie Holmes,Sarah Polley,Suzanne Krull,Desmon... | Comedy,Crime | United States | English | Told from three perspectives, a story of a bun... | 102 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 2 | 682 | Horse Girl | 2020 | 16 | 5.9 | 70 | Jeff Baena | Alison Brie,Molly Shannon,Goldenite,Stella Che... | Drama,Mystery,Thriller | United States | English | Sarah, a socially isolated arts and crafts sto... | 103 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 3 | 2325 | The Last Thing He Wanted | 2020 | 16 | 4.3 | 5 | Dee Rees | Anne Hathaway,Ben Affleck,Rosie Perez,Willem D... | Crime,Drama,Thriller | United States | English,Spanish,French | NA | 115 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 4 | 16245 | Dracula | 2020 | 16 | 7.4 | 74 | Francis Ford Coppola | Gary Oldman,Winona Ryder,Anthony Hopkins,Keanu... | Horror | United Kingdom,United States | English,Romanian,Greek,Bulgarian,Latin | In the wake of the Fronde in 1667, the French ... | 128 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
fig = px.bar(y = netflix_years_high_movies['Title'][:15],
x = netflix_years_high_movies['Year'][:15],
color = netflix_years_high_movies['Year'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Year : In Minutes'},
title = 'Movies with Highest Year in Minutes : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = netflix_years_low_movies['Title'][:15],
x = netflix_years_low_movies['Year'][:15],
color = netflix_years_low_movies['Year'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Year : In Minutes'},
title = 'Movies with Lowest Year in Minutes : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
hulu_years_high_movies = df_years_high_movies.loc[df_years_high_movies['Hulu']==1].reset_index()
hulu_years_high_movies = hulu_years_high_movies.drop(['index'], axis = 1)
hulu_years_low_movies = df_years_low_movies.loc[df_years_low_movies['Hulu']==1].reset_index()
hulu_years_low_movies = hulu_years_low_movies.drop(['index'], axis = 1)
hulu_years_high_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3589 | Big Time Adolescence | 2020 | 16 | 7 | 86 | Jason Orley | Griffin Gluck,Larry John Meyers,Michael Devine... | Comedy,Drama | United States | English | NA | 91 | movie | 0 | 1 | 0 | 0 | 0 | Hulu |
| 1 | 16550 | The Happy Days of Garry Marshall | 2020 | NR | 8.3 | NA | John Scheinfeld | Abigail Breslin,Anne Hathaway,Jennifer Garner,... | Documentary | United States | English | NA | 84 | movie | 0 | 1 | 0 | 0 | 0 | Hulu |
| 2 | 16570 | Drag Me | 2020 | 16 | 6.5 | NA | Sam Raimi | Alison Lohman,Justin Long,Lorna Raver,Dileep R... | Horror | United States | English,Spanish,Hungarian,Czech | NA | 99 | movie | 0 | 1 | 0 | 0 | 0 | Hulu |
| 3 | 3927 | Spaceship Earth | 2020 | NR | 6.4 | 89 | Matt Wolf | Shelley Taylor Morgan,Kathelin Gray,Marie Hard... | Documentary | United States | English | The true, stranger-than-fiction, adventure of ... | 113 | movie | 0 | 1 | 0 | 0 | 0 | Hulu |
| 4 | 4308 | Alien Contact | 2020 | NR | 7 | NA | Rico Lowry | Charles Washington | Documentary | United States | English | Actual UFO encounters between Alien spacecraft... | 75 | movie | 0 | 1 | 1 | 0 | 0 | Prime Video |
fig = px.bar(y = hulu_years_high_movies['Title'][:15],
x = hulu_years_high_movies['Year'][:15],
color = hulu_years_high_movies['Year'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Year : In Minutes'},
title = 'Movies with Highest Year in Minutes : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = hulu_years_low_movies['Title'][:15],
x = hulu_years_low_movies['Year'][:15],
color = hulu_years_low_movies['Year'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Year : In Minutes'},
title = 'Movies with Lowest Year in Minutes : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
prime_video_years_high_movies = df_years_high_movies.loc[df_years_high_movies['Prime Video']==1].reset_index()
prime_video_years_high_movies = prime_video_years_high_movies.drop(['index'], axis = 1)
prime_video_years_low_movies = df_years_low_movies.loc[df_years_low_movies['Prime Video']==1].reset_index()
prime_video_years_low_movies = prime_video_years_low_movies.drop(['index'], axis = 1)
prime_video_years_high_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6544 | Shubh Mangal Zyada Saavdhan | 2020 | NR | 5.8 | 92 | Hitesh Kewalya | Ayushmann Khurrana,Jitendra Kumar,Gajraj Rao,N... | Comedy,Romance | India | Hindi | An eccentric marketing guru visits a Coca-Cola... | 117 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 1 | 16641 | Russell Peters: Deported | 2020 | NR | 6.3 | NA | David Higby | Jason Collings,Vicky Kaushal,Taapsee Pannu,Rus... | Comedy | United States | English | NA | 67 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 2 | 6606 | Happiness Continues | 2020 | NR | 7.4 | NA | Anthony Mandler | Joe Jonas,Kevin Jonas,Nick Jonas,Priyanka Chop... | Documentary,Music | NA | NA | Blinded since childhood when a hideous car-cra... | 104 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 3 | 9400 | Killer Camera Monsters | 2020 | NR | 2.6 | NA | Ryan McBay | Sarati,Lauren Compton,Steve Filice,Bernadette ... | Horror,Thriller | United States | English | In Seattle, the successful forensic psychiatri... | 86 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 4 | 16694 | Jayde Adams: Serious Black Jumper | 2020 | NR | 7.1 | NA | Peter Orton | Jayde Adams | Comedy | United States | English | NA | 67 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
fig = px.bar(y = prime_video_years_high_movies['Title'][:15],
x = prime_video_years_high_movies['Year'][:15],
color = prime_video_years_high_movies['Year'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Year : In Minutes'},
title = 'Movies with Highest Year in Minutes : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = prime_video_years_low_movies['Title'][:15],
x = prime_video_years_low_movies['Year'][:15],
color = prime_video_years_low_movies['Year'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Year : In Minutes'},
title = 'Movies with Lowest Year in Minutes : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
disney_years_high_movies = df_years_high_movies.loc[df_years_high_movies['Disney+']==1].reset_index()
disney_years_high_movies = disney_years_high_movies.drop(['index'], axis = 1)
disney_years_low_movies = df_years_low_movies.loc[df_years_low_movies['Disney+']==1].reset_index()
disney_years_low_movies = disney_years_low_movies.drop(['index'], axis = 1)
disney_years_high_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16142 | A Celebration of the Music from Coco | 2020 | 13 | 7.1 | NA | Ron de Moraes | Benjamin Bratt,Jaime Camil,Aran de la Peña,Fel... | Music,Musical | United States | English | After six months of scientifically advanced tr... | 47 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ |
| 1 | 16080 | Lamp Life | 2020 | 13 | 6.7 | NA | Valerie LaPointe | Annie Potts,Ally Maki,Jim Hanks,Emily Davis,Mi... | Animation,Short,Adventure,Comedy,Family,Fantasy | United States | English | When Penny and her family are invited on a cru... | 7 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ |
| 2 | 15767 | Onward | 2020 | 7 | 7.4 | 88 | Dan Scanlon | Tom Holland,Chris Pratt,Julia Louis-Dreyfus,Oc... | Animation,Adventure,Comedy,Family,Fantasy | United States | English | The historical film by the American director E... | 102 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ |
| 3 | 16061 | The Disney Family Singalong | 2020 | 0 | 7.8 | NA | Hamish Hamilton,James B. Merryman | Christina Aguilera,Erin Andrews,Joshua Bassett... | Family,Musical | United States | English | After committing check fraud, Preston Waters l... | NA | movie | 0 | 0 | 0 | 1 | 0 | Disney+ |
| 4 | 16171 | Penguins: Life on the Edge | 2020 | 0 | 6.9 | NA | Alastair Fothergill,Jeff Wilson | Blair Underwood,Matthew Aeberhard,John Aitchis... | Documentary,Family | United States | English,French | In this film, edited from eight episodes of Di... | 78 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ |
fig = px.bar(y = disney_years_high_movies['Title'][:15],
x = disney_years_high_movies['Year'][:15],
color = disney_years_high_movies['Year'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Year : In Minutes'},
title = 'Movies with Highest Year in Minutes : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = disney_years_low_movies['Title'][:15],
x = disney_years_low_movies['Year'][:15],
color = disney_years_low_movies['Year'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Year : In Minutes'},
title = 'Movies with Lowest Year in Minutes : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
The Movie with Highest Year Ever Got is '{df_years_high_movies['Title'][0]}' : '{df_years_high_movies['Year'].max()}'\n
The Movie with Lowest Year Ever Got is '{df_years_low_movies['Title'][0]}' : '{df_years_low_movies['Year'].min()}'\n
The Movie with Highest Year on 'Netflix' is '{netflix_years_high_movies['Title'][0]}' : '{netflix_years_high_movies['Year'].max()}'\n
The Movie with Lowest Year on 'Netflix' is '{netflix_years_low_movies['Title'][0]}' : '{netflix_years_low_movies['Year'].min()}'\n
The Movie with Highest Year on 'Hulu' is '{hulu_years_high_movies['Title'][0]}' : '{hulu_years_high_movies['Year'].max()}'\n
The Movie with Lowest Year on 'Hulu' is '{hulu_years_low_movies['Title'][0]}' : '{hulu_years_low_movies['Year'].min()}'\n
The Movie with Highest Year on 'Prime Video' is '{prime_video_years_high_movies['Title'][0]}' : '{prime_video_years_high_movies['Year'].max()}'\n
The Movie with Lowest Year on 'Prime Video' is '{prime_video_years_low_movies['Title'][0]}' : '{prime_video_years_low_movies['Year'].min()}'\n
The Movie with Highest Year on 'Disney+' is '{disney_years_high_movies['Title'][0]}' : '{disney_years_high_movies['Year'].max()}'\n
The Movie with Lowest Year on 'Disney+' is '{disney_years_low_movies['Title'][0]}' : '{disney_years_low_movies['Year'].min()}'\n
''')
The Movie with Highest Year Ever Got is 'The Occupant' : '2020'
The Movie with Lowest Year Ever Got is 'Space: The New Frontier' : '1901'
The Movie with Highest Year on 'Netflix' is 'The Occupant' : '2020'
The Movie with Lowest Year on 'Netflix' is 'The Battle of Midway' : '1942'
The Movie with Highest Year on 'Hulu' is 'Big Time Adolescence' : '2020'
The Movie with Lowest Year on 'Hulu' is 'The Hunchback of Notre Dame' : '1923'
The Movie with Highest Year on 'Prime Video' is 'Shubh Mangal Zyada Saavdhan' : '2020'
The Movie with Lowest Year on 'Prime Video' is 'Space: The New Frontier' : '1901'
The Movie with Highest Year on 'Disney+' is 'A Celebration of the Music from Coco' : '2020'
The Movie with Lowest Year on 'Disney+' is 'The Three Musketeers' : '1921'
print(f'''
Accross All Platforms the Average Year is '{round(df_movies_years['Year'].mean(), ndigits = 2)}'\n
The Average Year on 'Netflix' is '{round(netflix_years_movies['Year'].mean(), ndigits = 2)}'\n
The Average Year on 'Hulu' is '{round(hulu_years_movies['Year'].mean(), ndigits = 2)}'\n
The Average Year on 'Prime Video' is '{round(prime_video_years_movies['Year'].mean(), ndigits = 2)}'\n
The Average Year on 'Disney+' is '{round(disney_years_movies['Year'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average Year is '2003.21'
The Average Year on 'Netflix' is '2013.26'
The Average Year on 'Hulu' is '2011.26'
The Average Year on 'Prime Video' is '2000.0'
The Average Year on 'Disney+' is '1997.97'
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_movies_years['Year'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_movies_years['Year'], ax = ax[1])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Year s Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_years_movies['Year'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_years_movies['Year'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_years_movies['Year'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_years_movies['Year'][:100], color = 'darkblue', legend = True, kde = True)
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
year_count = df_movies_years.groupby('Year')['Title'].count()
year_movies = df_movies_years.groupby('Year')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
year_data_movies = pd.concat([year_count, year_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count'})
year_data_movies = year_data_movies.sort_values(by = 'Movies Count', ascending = False)
# Movies Count per Year - All Platforms Combined
year_data_movies.head()
| Year | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 106 | 2017 | 1449 | 576 | 140 | 792 | 22 |
| 107 | 2018 | 1287 | 561 | 168 | 618 | 15 |
| 105 | 2016 | 1236 | 451 | 81 | 735 | 17 |
| 104 | 2015 | 1090 | 281 | 78 | 761 | 10 |
| 103 | 2014 | 991 | 174 | 60 | 774 | 10 |
fig = px.bar(y = year_data_movies['Movies Count'],
x = year_data_movies['Year'],
color = year_data_movies['Year'],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies Count', 'x' : 'Year : In Minutes'},
title = 'Movies with Year : All Platforms')
fig.update_layout(plot_bgcolor = "white")
fig.show()
fig = px.pie(year_data_movies[:10],
names = year_data_movies['Year'][:10],
values = year_data_movies['Movies Count'][:10],
color = year_data_movies['Movies Count'][:10],
color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textinfo = 'percent+label',
title = 'Movies Count based on Year Group')
fig.show()
# Highest Movies Count per Year - All Platforms Combined
df_year_high_movies = year_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_year_high_movies = df_year_high_movies.drop(['index'], axis = 1)
# filter = (year_data_movies['Movies Count'] = = (year_data_movies['Movies Count'].max()))
# df_year_high_movies = year_data_movies[filter]
# highest_rated_movies = year_data_movies.loc[year_data_movies['Movies Count'].idxmax()]
print('\nYear with Highest Ever Movies Count are : All Platforms Combined\n')
df_year_high_movies.head(5)
Year with Highest Ever Movies Count are : All Platforms Combined
| Year | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 2017 | 1449 | 576 | 140 | 792 | 22 |
| 1 | 2018 | 1287 | 561 | 168 | 618 | 15 |
| 2 | 2016 | 1236 | 451 | 81 | 735 | 17 |
| 3 | 2015 | 1090 | 281 | 78 | 761 | 10 |
| 4 | 2014 | 991 | 174 | 60 | 774 | 10 |
fig = px.bar(y = df_year_high_movies['Movies Count'][:10],
x = df_year_high_movies['Year'][:10],
color = df_year_high_movies['Movies Count'][:10],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies Count', 'x' : 'Year : In Minutes'},
title = 'Year with Highest Movies Count : All Platforms')
fig.update_layout(plot_bgcolor = "white")
fig.show()
# Lowest Movies Count per Year - All Platforms Combined
df_year_low_movies = year_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_year_low_movies = df_year_low_movies.drop(['index'], axis = 1)
# filter = (year_data_movies['Movies Count'] = = (year_data_movies['Movies Count'].min()))
# df_year_low_movies = year_data_movies[filter]
print('\nYear with Lowest Ever Movies Count are : All Platforms Combined\n')
df_year_low_movies.head(5)
Year with Lowest Ever Movies Count are : All Platforms Combined
| Year | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 1901 | 1 | 0 | 0 | 1 | 0 |
| 1 | 1912 | 1 | 0 | 0 | 1 | 0 |
| 2 | 1902 | 1 | 0 | 0 | 1 | 0 |
| 3 | 1927 | 1 | 0 | 0 | 1 | 0 |
| 4 | 1916 | 1 | 0 | 0 | 1 | 0 |
fig = px.bar(y = df_year_low_movies['Movies Count'][:10],
x = df_year_low_movies['Year'][:10],
color = df_year_low_movies['Movies Count'][:10],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies Count', 'x' : 'Year : In Minutes'},
title = 'Year with Lowest Movies Count : All Platforms')
fig.update_layout(plot_bgcolor = "white")
fig.show()
print(f'''
Total '{df_movies_years['Year'].count()}' Titles are available on All Platforms, out of which\n
You Can Choose to see Movies from Total '{year_data_movies['Year'].unique().shape[0]}' Year, They were Like this, \n
{year_data_movies.sort_values(by = 'Movies Count', ascending = False)['Year'].head(5).unique()} etc. \n
The Year with Highest Movies Count have '{year_data_movies['Movies Count'].max()}' Movies Available is '{df_year_high_movies['Year'][0]}', &\n
The Year with Lowest Movies Count have '{year_data_movies['Movies Count'].min()}' Movies Available is '{df_year_low_movies['Year'][0]}'
''')
Total '16923' Titles are available on All Platforms, out of which
You Can Choose to see Movies from Total '110' Year, They were Like this,
[2017 2018 2016 2015 2014] etc.
The Year with Highest Movies Count have '1449' Movies Available is '2017', &
The Year with Lowest Movies Count have '1' Movies Available is '1901'
# Highest Movies Count per Year - Netflix
netflix_year_movies = year_data_movies[year_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_year_movies = netflix_year_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
netflix_year_high_movies = df_year_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_year_high_movies = netflix_year_high_movies.drop(['index'], axis = 1)
netflix_year_low_movies = df_year_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_year_low_movies = netflix_year_low_movies.drop(['index'], axis = 1)
netflix_year_high_movies.head(5)
| Year | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 2017 | 1449 | 576 | 140 | 792 | 22 |
| 1 | 2018 | 1287 | 561 | 168 | 618 | 15 |
| 2 | 2016 | 1236 | 451 | 81 | 735 | 17 |
| 3 | 2019 | 703 | 430 | 119 | 164 | 24 |
| 4 | 2015 | 1090 | 281 | 78 | 761 | 10 |
# Highest Movies Count per Year - Hulu
hulu_year_movies = year_data_movies[year_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_year_movies = hulu_year_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_year_high_movies = df_year_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_year_high_movies = hulu_year_high_movies.drop(['index'], axis = 1)
hulu_year_low_movies = df_year_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_year_low_movies = hulu_year_low_movies.drop(['index'], axis = 1)
hulu_year_high_movies.head(5)
| Year | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 2018 | 1287 | 561 | 168 | 618 | 15 |
| 1 | 2017 | 1449 | 576 | 140 | 792 | 22 |
| 2 | 2019 | 703 | 430 | 119 | 164 | 24 |
| 3 | 2016 | 1236 | 451 | 81 | 735 | 17 |
| 4 | 2015 | 1090 | 281 | 78 | 761 | 10 |
# Highest Movies Count per Year - Prime Video
prime_video_year_movies = year_data_movies[year_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_year_movies = prime_video_year_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
prime_video_year_high_movies = df_year_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_year_high_movies = prime_video_year_high_movies.drop(['index'], axis = 1)
prime_video_year_low_movies = df_year_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_year_low_movies = prime_video_year_low_movies.drop(['index'], axis = 1)
prime_video_year_high_movies.head(5)
| Year | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 2013 | 979 | 139 | 51 | 815 | 12 |
| 1 | 2017 | 1449 | 576 | 140 | 792 | 22 |
| 2 | 2014 | 991 | 174 | 60 | 774 | 10 |
| 3 | 2015 | 1090 | 281 | 78 | 761 | 10 |
| 4 | 2016 | 1236 | 451 | 81 | 735 | 17 |
# Highest Movies Count per Year - Disney+
disney_year_movies = year_data_movies[year_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_year_movies = disney_year_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
disney_year_high_movies = df_year_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_year_high_movies = disney_year_high_movies.drop(['index'], axis = 1)
disney_year_low_movies = df_year_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_year_low_movies = disney_year_low_movies.drop(['index'], axis = 1)
disney_year_high_movies.head(5)
| Year | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 2003 | 214 | 31 | 6 | 157 | 25 |
| 1 | 2019 | 703 | 430 | 119 | 164 | 24 |
| 2 | 2017 | 1449 | 576 | 140 | 792 | 22 |
| 3 | 2000 | 176 | 19 | 8 | 132 | 21 |
| 4 | 2002 | 196 | 21 | 9 | 152 | 21 |
print(f'''
The Year with Highest Movies Count Ever Got is '{df_year_high_movies['Year'][0]}' : '{df_year_high_movies['Movies Count'].max()}'\n
The Year with Lowest Movies Count Ever Got is '{df_year_low_movies['Year'][0]}' : '{df_year_low_movies['Movies Count'].min()}'\n
The Year with Highest Movies Count on 'Netflix' is '{netflix_year_high_movies['Year'][0]}' : '{netflix_year_high_movies['Netflix'].max()}'\n
The Year with Lowest Movies Count on 'Netflix' is '{netflix_year_low_movies['Year'][0]}' : '{netflix_year_low_movies['Netflix'].min()}'\n
The Year with Highest Movies Count on 'Hulu' is '{hulu_year_high_movies['Year'][0]}' : '{hulu_year_high_movies['Hulu'].max()}'\n
The Year with Lowest Movies Count on 'Hulu' is '{hulu_year_low_movies['Year'][0]}' : '{hulu_year_low_movies['Hulu'].min()}'\n
The Year with Highest Movies Count on 'Prime Video' is '{prime_video_year_high_movies['Year'][0]}' : '{prime_video_year_high_movies['Prime Video'].max()}'\n
The Year with Lowest Movies Count on 'Prime Video' is '{prime_video_year_low_movies['Year'][0]}' : '{prime_video_year_low_movies['Prime Video'].min()}'\n
The Year with Highest Movies Count on 'Disney+' is '{disney_year_high_movies['Year'][0]}' : '{disney_year_high_movies['Disney+'].max()}'\n
The Year with Lowest Movies Count on 'Disney+' is '{disney_year_low_movies['Year'][0]}' : '{disney_year_low_movies['Disney+'].min()}'\n
''')
The Year with Highest Movies Count Ever Got is '2017' : '1449'
The Year with Lowest Movies Count Ever Got is '1901' : '1'
The Year with Highest Movies Count on 'Netflix' is '2017' : '576'
The Year with Lowest Movies Count on 'Netflix' is '1941' : '0'
The Year with Highest Movies Count on 'Hulu' is '2018' : '168'
The Year with Lowest Movies Count on 'Hulu' is '1941' : '0'
The Year with Highest Movies Count on 'Prime Video' is '2013' : '815'
The Year with Lowest Movies Count on 'Prime Video' is '1901' : '1'
The Year with Highest Movies Count on 'Disney+' is '2003' : '25'
The Year with Lowest Movies Count on 'Disney+' is '1901' : '0'
print(f'''
Accross All Platforms the Average Movies Count of Year is '{round(year_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
The Average Movies Count of Year on 'Netflix' is '{round(netflix_year_movies['Netflix'].mean(), ndigits = 2)}'\n
The Average Movies Count of Year on 'Hulu' is '{round(hulu_year_movies['Hulu'].mean(), ndigits = 2)}'\n
The Average Movies Count of Year on 'Prime Video' is '{round(prime_video_year_movies['Prime Video'].mean(), ndigits = 2)}'\n
The Average Movies Count of Year on 'Disney+' is '{round(disney_year_movies['Disney+'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average Movies Count of Year is '153.85'
The Average Movies Count of Year on 'Netflix' is '58.66'
The Average Movies Count of Year on 'Hulu' is '16.83'
The Average Movies Count of Year on 'Prime Video' is '111.88'
The Average Movies Count of Year on 'Disney+' is '6.76'
print(f'''
Accross All Platforms Total Count of Year is '{year_data_movies['Year'].unique().shape[0]}'\n
Total Count of Year on 'Netflix' is '{netflix_year_movies['Year'].unique().shape[0]}'\n
Total Count of Year on 'Hulu' is '{hulu_year_movies['Year'].unique().shape[0]}'\n
Total Count of Year on 'Prime Video' is '{prime_video_year_movies['Year'].unique().shape[0]}'\n
Total Count of Year on 'Disney+' is '{disney_year_movies['Year'].unique().shape[0]}'\n
''')
Accross All Platforms Total Count of Year is '110'
Total Count of Year on 'Netflix' is '62'
Total Count of Year on 'Hulu' is '63'
Total Count of Year on 'Prime Video' is '110'
Total Count of Year on 'Disney+' is '83'
fig = plt.figure(figsize = (20, 10))
sns.lineplot(data = year_data_movies, x = 'Year', y = 'Movies Count')
plt.show()
plt.figure(figsize = (20, 10))
sns.lineplot(x = year_data_movies['Year'], y = year_data_movies['Netflix'], color = 'red')
sns.lineplot(x = year_data_movies['Year'], y = year_data_movies['Hulu'], color = 'lightgreen')
sns.lineplot(x = year_data_movies['Year'], y = year_data_movies['Prime Video'], color = 'lightblue')
sns.lineplot(x = year_data_movies['Year'], y = year_data_movies['Disney+'], color = 'darkblue')
plt.xlabel('Release Year', fontsize = 15)
plt.ylabel('Movies Count', fontsize = 15)
plt.show()
fig, axes = plt.subplots(2, 2,figsize=(20 ,20))
n_y_ax1 = sns.lineplot(x = year_data_movies['Year'], y = year_data_movies['Netflix'], color = 'red', ax = axes[0, 0])
h_y_ax2 = sns.lineplot(x = year_data_movies['Year'], y = year_data_movies['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_y_ax3 = sns.lineplot(x = year_data_movies['Year'], y = year_data_movies['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_y_ax4 = sns.lineplot(x = year_data_movies['Year'], y = year_data_movies['Disney+'], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_y_ax1.title.set_text(labels[0])
h_y_ax2.title.set_text(labels[1])
p_y_ax3.title.set_text(labels[2])
d_y_ax4.title.set_text(labels[3])
plt.show()
def round_val(data):
if str(data) != 'nan':
return round(data)
def round_fix(data):
if data in range(1801,1901):
# print(data)
return 1900
if data in range(1901,1911):
return 1910
if data in range(1911,1921):
return 1920
if data in range(1921,1931):
return 1930
if data in range(1931,1941):
return 1940
if data in range(1941,1951):
return 1950
if data in range(1951,1961):
return 1960
if data in range(1961,1971):
return 1970
if data in range(1971,1981):
return 1980
if data in range(1981,1991):
return 1990
if data in range(1991,2001):
return 2000
if data in range(2000,2011):
return 2010
if data in range(2010,2021):
return 2020
if data in range(2020,2031):
return 2030
else:
return 2100
df_movies_years_group['Year Group'] = df_movies_years_group['Year'].apply(round_fix).astype(int)
years_values = df_movies_years_group['Year Group'].value_counts().sort_index(ascending = False).tolist()
years_index = df_movies_years_group['Year Group'].value_counts().sort_index(ascending = False).index
# years_values, years_index
years_group_count = df_movies_years_group.groupby('Year Group')['Title'].count()
years_group_movies = df_movies_years_group.groupby('Year Group')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
years_group_data_movies = pd.concat([years_group_count, years_group_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count'})
years_group_data_movies = years_group_data_movies.sort_values(by = 'Movies Count', ascending = False)
# Year Group with Movies Counts - All Platforms Combined
years_group_data_movies.sort_values(by = 'Movies Count', ascending = False)
| Year Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 11 | 2020 | 9410 | 2939 | 795 | 5935 | 155 |
| 10 | 2010 | 3312 | 444 | 145 | 2650 | 174 |
| 9 | 2000 | 1119 | 131 | 56 | 872 | 105 |
| 7 | 1980 | 785 | 35 | 15 | 725 | 33 |
| 8 | 1990 | 746 | 69 | 30 | 652 | 32 |
| 6 | 1970 | 385 | 6 | 6 | 357 | 21 |
| 3 | 1940 | 369 | 0 | 2 | 363 | 4 |
| 4 | 1950 | 366 | 11 | 3 | 352 | 10 |
| 5 | 1960 | 366 | 2 | 7 | 338 | 23 |
| 2 | 1930 | 44 | 0 | 1 | 42 | 4 |
| 1 | 1920 | 19 | 0 | 0 | 19 | 0 |
| 0 | 1910 | 2 | 0 | 0 | 2 | 0 |
years_group_data_movies.sort_values(by = 'Year Group', ascending = False)
| Year Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 11 | 2020 | 9410 | 2939 | 795 | 5935 | 155 |
| 10 | 2010 | 3312 | 444 | 145 | 2650 | 174 |
| 9 | 2000 | 1119 | 131 | 56 | 872 | 105 |
| 8 | 1990 | 746 | 69 | 30 | 652 | 32 |
| 7 | 1980 | 785 | 35 | 15 | 725 | 33 |
| 6 | 1970 | 385 | 6 | 6 | 357 | 21 |
| 5 | 1960 | 366 | 2 | 7 | 338 | 23 |
| 4 | 1950 | 366 | 11 | 3 | 352 | 10 |
| 3 | 1940 | 369 | 0 | 2 | 363 | 4 |
| 2 | 1930 | 44 | 0 | 1 | 42 | 4 |
| 1 | 1920 | 19 | 0 | 0 | 19 | 0 |
| 0 | 1910 | 2 | 0 | 0 | 2 | 0 |
fig = px.bar(y = years_group_data_movies['Movies Count'],
x = years_group_data_movies['Year Group'],
color = years_group_data_movies['Year Group'],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies Count', 'x' : 'Year : In Minutes'},
title = 'Movies with Group Year in Minutes : All Platforms')
fig.update_layout(plot_bgcolor = "white")
fig.show()
fig = px.pie(years_group_data_movies[:10],
names = years_group_data_movies['Year Group'],
values = years_group_data_movies['Movies Count'],
color = years_group_data_movies['Movies Count'],
color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textinfo = 'percent+label',
title = 'Movies Count based on Year Group')
fig.show()
df_years_group_high_movies = years_group_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_years_group_high_movies = df_years_group_high_movies.drop(['index'], axis = 1)
# filter = (years_group_data_movies['Movies Count'] == (years_group_data_movies['Movies Count'].max()))
# df_years_group_high_movies = years_group_data_movies[filter]
# highest_rated_movies = years_group_data_movies.loc[years_group_data_movies['Movies Count'].idxmax()]
# print('\nYear with Highest Ever Movies Count are : All Platforms Combined\n')
df_years_group_high_movies.head(5)
| Year Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 2020 | 9410 | 2939 | 795 | 5935 | 155 |
| 1 | 2010 | 3312 | 444 | 145 | 2650 | 174 |
| 2 | 2000 | 1119 | 131 | 56 | 872 | 105 |
| 3 | 1980 | 785 | 35 | 15 | 725 | 33 |
| 4 | 1990 | 746 | 69 | 30 | 652 | 32 |
df_years_group_low_movies = years_group_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_years_group_low_movies = df_years_group_low_movies.drop(['index'], axis = 1)
# filter = (years_group_data_movies['Movies Count'] = = (years_group_data_movies['Movies Count'].min()))
# df_years_group_low_movies = years_group_data_movies[filter]
# print('\nYear with Lowest Ever Movies Count are : All Platforms Combined\n')
df_years_group_low_movies.head(5)
| Year Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 1910 | 2 | 0 | 0 | 2 | 0 |
| 1 | 1920 | 19 | 0 | 0 | 19 | 0 |
| 2 | 1930 | 44 | 0 | 1 | 42 | 4 |
| 3 | 1950 | 366 | 11 | 3 | 352 | 10 |
| 4 | 1960 | 366 | 2 | 7 | 338 | 23 |
print(f'''
Total '{df_movies_years['Year'].count()}' Titles are available on All Platforms, out of which\n
You Can Choose to see Movies from Total '{years_group_data_movies['Year Group'].unique().shape[0]}' Year Group, They were Like this, \n
{years_group_data_movies.sort_values(by = 'Movies Count', ascending = False)['Year Group'].unique()} etc. \n
The Year Group with Highest Movies Count have '{years_group_data_movies['Movies Count'].max()}' Movies Available is '{df_years_group_high_movies['Year Group'][0]}', &\n
The Year Group with Lowest Movies Count have '{years_group_data_movies['Movies Count'].min()}' Movies Available is '{df_years_group_low_movies['Year Group'][0]}'
''')
Total '16923' Titles are available on All Platforms, out of which
You Can Choose to see Movies from Total '12' Year Group, They were Like this,
[2020 2010 2000 1980 1990 1970 1940 1950 1960 1930 1920 1910] etc.
The Year Group with Highest Movies Count have '9410' Movies Available is '2020', &
The Year Group with Lowest Movies Count have '2' Movies Available is '1910'
netflix_years_group_movies = years_group_data_movies[years_group_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_years_group_movies = netflix_years_group_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
netflix_years_group_high_movies = df_years_group_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_years_group_high_movies = netflix_years_group_high_movies.drop(['index'], axis = 1)
netflix_years_group_low_movies = df_years_group_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_years_group_low_movies = netflix_years_group_low_movies.drop(['index'], axis = 1)
netflix_years_group_high_movies.head(5)
| Year Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 2020 | 9410 | 2939 | 795 | 5935 | 155 |
| 1 | 2010 | 3312 | 444 | 145 | 2650 | 174 |
| 2 | 2000 | 1119 | 131 | 56 | 872 | 105 |
| 3 | 1990 | 746 | 69 | 30 | 652 | 32 |
| 4 | 1980 | 785 | 35 | 15 | 725 | 33 |
hulu_years_group_movies = years_group_data_movies[years_group_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_years_group_movies = hulu_years_group_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_years_group_high_movies = df_years_group_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_years_group_high_movies = hulu_years_group_high_movies.drop(['index'], axis = 1)
hulu_years_group_low_movies = df_years_group_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_years_group_low_movies = hulu_years_group_low_movies.drop(['index'], axis = 1)
hulu_years_group_high_movies.head(5)
| Year Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 2020 | 9410 | 2939 | 795 | 5935 | 155 |
| 1 | 2010 | 3312 | 444 | 145 | 2650 | 174 |
| 2 | 2000 | 1119 | 131 | 56 | 872 | 105 |
| 3 | 1990 | 746 | 69 | 30 | 652 | 32 |
| 4 | 1980 | 785 | 35 | 15 | 725 | 33 |
prime_video_years_group_movies = years_group_data_movies[years_group_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_years_group_movies = prime_video_years_group_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
prime_video_years_group_high_movies = df_years_group_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_years_group_high_movies = prime_video_years_group_high_movies.drop(['index'], axis = 1)
prime_video_years_group_low_movies = df_years_group_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_years_group_low_movies = prime_video_years_group_low_movies.drop(['index'], axis = 1)
prime_video_years_group_high_movies.head(5)
| Year Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 2020 | 9410 | 2939 | 795 | 5935 | 155 |
| 1 | 2010 | 3312 | 444 | 145 | 2650 | 174 |
| 2 | 2000 | 1119 | 131 | 56 | 872 | 105 |
| 3 | 1980 | 785 | 35 | 15 | 725 | 33 |
| 4 | 1990 | 746 | 69 | 30 | 652 | 32 |
disney_years_group_movies = years_group_data_movies[years_group_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_years_group_movies = disney_years_group_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
disney_years_group_high_movies = df_years_group_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_years_group_high_movies = disney_years_group_high_movies.drop(['index'], axis = 1)
disney_years_group_low_movies = df_years_group_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_years_group_low_movies = disney_years_group_low_movies.drop(['index'], axis = 1)
disney_years_group_high_movies.head(5)
| Year Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 2010 | 3312 | 444 | 145 | 2650 | 174 |
| 1 | 2020 | 9410 | 2939 | 795 | 5935 | 155 |
| 2 | 2000 | 1119 | 131 | 56 | 872 | 105 |
| 3 | 1980 | 785 | 35 | 15 | 725 | 33 |
| 4 | 1990 | 746 | 69 | 30 | 652 | 32 |
print(f'''
The Year Group with Highest Movies Count Ever Got is '{df_years_group_high_movies['Year Group'][0]}' : '{df_years_group_high_movies['Movies Count'].max()}'\n
The Year Group with Lowest Movies Count Ever Got is '{df_years_group_low_movies['Year Group'][0]}' : '{df_years_group_low_movies['Movies Count'].min()}'\n
The Year Group with Highest Movies Count on 'Netflix' is '{netflix_years_group_high_movies['Year Group'][0]}' : '{netflix_years_group_high_movies['Netflix'].max()}'\n
The Year Group with Lowest Movies Count on 'Netflix' is '{netflix_years_group_low_movies['Year Group'][0]}' : '{netflix_years_group_low_movies['Netflix'].min()}'\n
The Year Group with Highest Movies Count on 'Hulu' is '{hulu_years_group_high_movies['Year Group'][0]}' : '{hulu_years_group_high_movies['Hulu'].max()}'\n
The Year Group with Lowest Movies Count on 'Hulu' is '{hulu_years_group_low_movies['Year Group'][0]}' : '{hulu_years_group_low_movies['Hulu'].min()}'\n
The Year Group with Highest Movies Count on 'Prime Video' is '{prime_video_years_group_high_movies['Year Group'][0]}' : '{prime_video_years_group_high_movies['Prime Video'].max()}'\n
The Year Group with Lowest Movies Count on 'Prime Video' is '{prime_video_years_group_low_movies['Year Group'][0]}' : '{prime_video_years_group_low_movies['Prime Video'].min()}'\n
The Year Group with Highest Movies Count on 'Disney+' is '{disney_years_group_high_movies['Year Group'][0]}' : '{disney_years_group_high_movies['Disney+'].max()}'\n
The Year Group with Lowest Movies Count on 'Disney+' is '{disney_years_group_low_movies['Year Group'][0]}' : '{disney_years_group_low_movies['Disney+'].min()}'\n
''')
The Year Group with Highest Movies Count Ever Got is '2020' : '9410'
The Year Group with Lowest Movies Count Ever Got is '1910' : '2'
The Year Group with Highest Movies Count on 'Netflix' is '2020' : '2939'
The Year Group with Lowest Movies Count on 'Netflix' is '1940' : '0'
The Year Group with Highest Movies Count on 'Hulu' is '2020' : '795'
The Year Group with Lowest Movies Count on 'Hulu' is '1920' : '0'
The Year Group with Highest Movies Count on 'Prime Video' is '2020' : '5935'
The Year Group with Lowest Movies Count on 'Prime Video' is '1910' : '2'
The Year Group with Highest Movies Count on 'Disney+' is '2010' : '174'
The Year Group with Lowest Movies Count on 'Disney+' is '1920' : '0'
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_ru_ax1 = sns.barplot(x = netflix_years_group_movies['Year Group'], y = netflix_years_group_movies['Netflix'], palette = 'Reds_r', ax = axes[0, 0])
h_ru_ax2 = sns.barplot(x = hulu_years_group_movies['Year Group'], y = hulu_years_group_movies['Hulu'], palette = 'Greens_r', ax = axes[0, 1])
p_ru_ax3 = sns.barplot(x = prime_video_years_group_movies['Year Group'], y = prime_video_years_group_movies['Prime Video'], palette = 'Blues_r', ax = axes[1, 0])
d_ru_ax4 = sns.barplot(x = disney_years_group_movies['Year Group'], y = disney_years_group_movies['Disney+'], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_ru_ax1.title.set_text(labels[0])
h_ru_ax2.title.set_text(labels[1])
p_ru_ax3.title.set_text(labels[2])
d_ru_ax4.title.set_text(labels[3])
plt.show()
plt.figure(figsize = (20, 5))
sns.lineplot(x = years_group_data_movies['Year Group'], y = years_group_data_movies['Netflix'], color = 'red')
sns.lineplot(x = years_group_data_movies['Year Group'], y = years_group_data_movies['Hulu'], color = 'lightgreen')
sns.lineplot(x = years_group_data_movies['Year Group'], y = years_group_data_movies['Prime Video'], color = 'lightblue')
sns.lineplot(x = years_group_data_movies['Year Group'], y = years_group_data_movies['Disney+'], color = 'darkblue')
plt.xlabel('Year Group', fontsize = 15)
plt.ylabel('Movies Count', fontsize = 15)
plt.show()
print(f'''
Accross All Platforms Total Count of Year Group is '{years_group_data_movies['Year Group'].unique().shape[0]}'\n
Total Count of Year Group on 'Netflix' is '{netflix_years_group_movies['Year Group'].unique().shape[0]}'\n
Total Count of Year Group on 'Hulu' is '{hulu_years_group_movies['Year Group'].unique().shape[0]}'\n
Total Count of Year Group on 'Prime Video' is '{prime_video_years_group_movies['Year Group'].unique().shape[0]}'\n
Total Count of Year Group on 'Disney+' is '{disney_years_group_movies['Year Group'].unique().shape[0]}'\n
''')
Accross All Platforms Total Count of Year Group is '12'
Total Count of Year Group on 'Netflix' is '8'
Total Count of Year Group on 'Hulu' is '10'
Total Count of Year Group on 'Prime Video' is '12'
Total Count of Year Group on 'Disney+' is '10'
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_ru_ax1 = sns.lineplot(y = years_group_data_movies['Year Group'], x = years_group_data_movies['Netflix'], color = 'red', ax = axes[0, 0])
h_ru_ax2 = sns.lineplot(y = years_group_data_movies['Year Group'], x = years_group_data_movies['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_ru_ax3 = sns.lineplot(y = years_group_data_movies['Year Group'], x = years_group_data_movies['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_ru_ax4 = sns.lineplot(y = years_group_data_movies['Year Group'], x = years_group_data_movies['Disney+'], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_ru_ax1.title.set_text(labels[0])
h_ru_ax2.title.set_text(labels[1])
p_ru_ax3.title.set_text(labels[2])
d_ru_ax4.title.set_text(labels[3])
plt.show()
fig, axes = plt.subplots(2, 2,figsize=(20 ,20))
n_yg_ax1 = sns.lineplot(x = years_group_data_movies['Year Group'], y = years_group_data_movies['Netflix'], color = 'red', ax = axes[0, 0])
h_yg_ax2 = sns.lineplot(x = years_group_data_movies['Year Group'], y = years_group_data_movies['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_yg_ax3 = sns.lineplot(x = years_group_data_movies['Year Group'], y = years_group_data_movies['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_yg_ax4 = sns.lineplot(x = years_group_data_movies['Year Group'], y = years_group_data_movies['Disney+'], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_yg_ax1.title.set_text(labels[0])
h_yg_ax2.title.set_text(labels[1])
p_yg_ax3.title.set_text(labels[2])
d_yg_ax4.title.set_text(labels[3])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_ru_ax1 = sns.barplot(x = years_group_data_movies['Year Group'], y = years_group_data_movies['Netflix'], palette = 'Reds_r', ax = axes[0, 0])
h_ru_ax2 = sns.barplot(x = years_group_data_movies['Year Group'], y = years_group_data_movies['Hulu'], palette = 'Greens_r', ax = axes[0, 1])
p_ru_ax3 = sns.barplot(x = years_group_data_movies['Year Group'], y = years_group_data_movies['Prime Video'], palette = 'Blues_r', ax = axes[1, 0])
d_ru_ax4 = sns.barplot(x = years_group_data_movies['Year Group'], y = years_group_data_movies['Disney+'], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_ru_ax1.title.set_text(labels[0])
h_ru_ax2.title.set_text(labels[1])
p_ru_ax3.title.set_text(labels[2])
d_ru_ax4.title.set_text(labels[3])
plt.show()